"Master data! Master data! My supply chain for master data!"

With data quality and consistency becoming critically important factors in supply chain performance, companies will have to pay more attention to master data management. That may require supply chain managers to change the way they think about and utilize data.

"A horse! A horse! My kingdom for a horse!" screams King Richard III in Shakespeare's play of that name. At that point in the play, Richard, unhorsed and fighting on foot, is put at a disadvantage on the field of battle at Bosworth; as a result, he is killed by Richmond, who then succeeds to the throne as Henry VII. The point I am making here and with the title of this article is that the availability of a critical resource (like a horse, in Richard's case, or master data, for a supply chain) can be crucial for success and even for survival.

We at Gartner define master data management (MDM) as a technology-enabled business discipline in which business and information technology (IT) must work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of an enterprise's official, shared master data assets. Supply chain performance is dependent on consistent definitions of customers, products, items, locations, and other master data objects. When data is poorly governed and inconsistent, supply chains become less competitive because more time and money is spent on managing information between systems and trading partners, and less is available for innovation. Good data leads to efficient supply chains, allowing resources to be spent on innovation rather than on coping with problems.

Master data has always been necessary, but the importance of its consistency in supply chains is growing. There are three main reasons for this. First, supply chain performance is coming under an increasing number of pressures. These include global and local competition; legal and regulatory demands; and social responsibility-minded shareholders, to name just a few of many possible examples. Underlying them all are today's fragile economic conditions.

Second, there is a growing emphasis among many organizations on knowing their customers' needs. More than this, organizations are seeking to influence the behavior of customers and prospects, guiding customers' purchasing decisions toward their own products and services and away from those of competitors. This change in focus is leading to a greater demand for and reliance on consistent data. For any supply chain leader, the path to meeting those demands leads back to master data.

And third, given the level of attention that IT is placing on data consistency, as well as companies' growing focus on collaboration with trading partners and their need to improve business outcomes, data consistency—especially between trading partners—is increasingly a prerequisite for improved and competitive supply chain performance.

As data quality and consistency become increasingly important factors in supply chain performance, companies that want to catch up with the innovators will have to pay closer attention to master data management. That may require supply chain managers to change the way they think about and utilize data. With that in mind, here are four topics that should be on the joint information management and supply chain agenda for 2013.

1. Business outcomes trump data quality

I am playing with words here. Of course data quality is important. But how important should it be? That is, how much cost should you incur to improve data quality, and what business value will you realize from that effort? Programs like master data management are most successful when they have a clear line of sight to a specific and measurable business outcome. By contrast, organizations seem to struggle when they focus on data quality and metrics related to the data itself as measures of success.

An example of a "bad" (ineffective) MDM metric would be "the number of de-duplicated records per month." This is of little interest to the user of business information, and it does not help the business understand why changing the way it uses information would improve outcomes. An example of a "good" (effective) MDM metric would be "net new revenue per first six weeks of new product introduction." This information will be relevant to the business user—the word "revenue" will make sure of that. Moreover, there is a specific time frame; the metric is bounded so that it can be measured. The number of de-duped records is not irrelevant, as de-duping would improve the quality of the data being used. But adopting the "net new revenue" metric will, rightly, keep the focus on the relationships between various activities and the outcomes of the work taking place, rather than on the data itself.

2. Information governance: Less about control and more about information value

Organizations are making progress with master data management and other information governance programs, but we are still seeing great resistance to these efforts. One reason for that resistance is that users often misunderstand what "information governance" means. Many organizations equate governance with rules, regulations, and "Big Brother" (management that exerts excessive control) limiting flexibility in how the business handles data and what it can do with it. However, a more informative interpretation of information governance recognizes that the focus should be more on identifying what data is most useful to the business and its desired business outcomes, and on designing processes that are as flexible as the business needs them to be.

When asked what the term means to them, however, business users we regularly speak with have offered many different definitions, including: security, access, control, rigidity, limited flexibility, IT managers or "Big Brother" watching, extra work, "something focused on data that IT needs to work with," "something we are doing wrong (apparently)," and "not related to what we do in the business." (The very word governance, which implies control from above, may be partly responsible for those attitudes. Replacing it with terms such as "stewardship" or "custodianship" might help to allay any fears users have in that regard.)

These responses reflect a dated, negative view of what information governance is about. Today, a much different approach is called for. No one, for instance, should design a governance process that is rigid; instead, the process and supporting organization should be as flexible as the business needs them to be. Security and access, moreover, will be policies of interest to the work being done, but they should not be the only or even the main focus of governance. Instead, they are now secondary or tertiary concerns.

In addition, information governance should only be undertaken when a business has both a desire to first, govern data for the express purpose of realizing business value, and second, a willingness to change its business processes that create, enrich, approve, or otherwise use data, so that it can extract that value. For example, business users who had previously been reluctant to participate in the governance of customer data would be willing to do so if it would help them achieve their own, measured objectives.

Unfortunately, people do not always recognize the potential benefits of information governance. Consider the example of an employee like "Fred." You all know who Fred is. He has been with your supply chain group for years; he does not "like" IT and IT does not "like" him. But when it is 4:45 p.m. on a Friday and the information system will not allow you to ship an order, you go to Fred to find out how to get that order out the door. Fred knows that if you enter "00" in the field that is at fault, it will override the system and allow the order to ship! Fred is the authority and the informal steward of information. He and his like are governing information every day. But information governance today is not focused on stopping what Fred is doing. Instead, it is focused on understanding what is wrong with a process and its supporting application, and on changing them to enable better outcomes—such as shipping orders complete and on time.

As this all-too-common example suggests, we need to avoid the emotional inhibitions related to terms and concepts like "information governance" and just get the job done.

The growing hype about "big data" analysis is leading organizations to ask themselves: Is there any way we can monetize our information? Can we use information not only in our own business but actually sell some aspect of it to others?

Some companies are already doing this. A few years ago Gartner published a case study about how Best Buy was, at the time, selling access to application programming interfaces (APIs) that published product-attribute information for use in marketing aggregators' online shopping sites. This data originally was provided, in part, to Best Buy by its suppliers. Those same suppliers, meanwhile, were working on ways to monetize their own product-related data. Thus, while manufacturers and their retail partners may primarily sell products like televisions and Blu-Ray players, they can also create an additional revenue stream by selling some of their information.

The Best Buy example and more-recent stories, such as how Netflix is able to mine insights from when and how frequently customers pause their video players, illustrate how information can be accounted for as an asset. This concept is beginning to attract more attention. But information is not yet considered to be an acceptable intangible asset for accounting purposes, so the monetary value of a company's unique customer master list remains unaccounted for.

If you accept, however, that your organization's information assets have financial value, then a host of questions will open up. Which information asset should you invest in most? Which information assets and information management or exploiting programs will yield the greatest returns? Should you keep information assets on the assumption that they will pay you a higher return later? Do you invest in enterprise resource planning (ERP) or business intelligence (BI) systems, and in which order? What about master data management? These are hard questions to answer. But it is these questions your IT group must be able to answer—and does so (perhaps informally) as it communicates what its priorities are in support of a particular business goal.

4. Making information governance "stick"

To address the issues discussed above, companies finally are starting to have more down-to-earth conversations about data governance. Many leading and next-leading organizations are appointing or hiring "data stewards" and are establishing business process and business data owners and data governance bodies. They may subsequently adopt master data management technology, perhaps coupled with a business process management (BPM) initiative. The scope of such initiatives, moreover, is often dictated by a broad and strategic focus on supply chain performance. That's what has been happening in 2012 and 2013. Why, then, are we not hearing more about successful MDM projects?

In fact, there are MDM success stories, but not every implementation is going as well as everyone would like. One scenario we have been seeing recently is what I would characterize as being unable to make information governance "stick." The situation typically looks like this:

Implementation is complete.

Applications have been integrated; data is flowing.

We hired data stewards (within the business, in fact).

There was or is a governance board; they met a few times—we think.

Now it's three months since "go live" and the project team has disbanded.

Exceptions are emerging in the data, and the business users are coming to IT for resolution. IT does not know what to do with the exceptions, and business users can't understand the language of the messages.

There are two reasons companies find themselves in this kind of situation. The first is that some organizations are struggling to get sufficient buy-in for the new roles and responsibilities required for governance (policy setting) and stewardship (policy enforcement). They initiate the necessary work as part of the implementation but do not seem to carry through with it day to day.

The second is that too many so-called master data management software vendors and their tools are not mature enough to adequately support business-led data stewardship. When I worked in industry (in consumer goods, industrial manufacturing, and white goods) in the days before information governance had been formally defined, I figured out how to use product data to do my job better. Sometimes that meant discovering what kind of data exceptions could be used to override the system. But the tools I used were rudimentary, even manual. Today's master data management solutions would not have been useful to stewards of supply chain product data like me, or to my current-day counterparts. Too much emphasis is being placed now on data quality, matching, integration, and modeling. And too little is being placed on the monitoring and problem-solving tools that business data stewards need in order to carry out their day-to-day work.

The role of data steward, by the way, should not be an onerous one. In fact, it should not be a full-time job. If it is, then the organization is focusing on the wrong things. Problem solving for business process outcomes that are held back by data problems that the IT group cannot handle should take no more than a few minutes each week. How many minutes may differ for each organization—it might be 15 minutes, or 20, or 10. The number is not the point; the point is that this responsibility should take up a very small amount of time compared to the rest of a business user's work.

One other important point is that data maintenance is different from data stewardship. Too many users and vendors do not understand that these roles, and the work associated with them, can and should be separated. Who actually creates the data is not so important; that work could be done as a shared service, or it could even be outsourced. But the role of steward—that is, the chief problem solver—cannot be outsourced or removed from the line of business that is affected by the data problem.

Winning with data management
The four issues discussed in this article are the largest and most notable of the trends related to master data management and information governance that will play out in supply chains across the globe. There is one important point I must re-emphasize. The supply chains that will win in the next few years won't come out on top simply because they have the best information. All of them, I believe, will do something more with their data: They will successfully tie their information management disciplines to specific and measurable business outcomes.

As trading partners continue to deepen their collaborative relationships, seek to better understand their customers and end consumers, and focus on ever more demand-driven supply chain strategies, the consistency of the data that resides within corporate systems and is shared with partners will become even more critical than it is now. Businesses will need to govern their information to a degree that will ensure the integrity of their supply chain strategies—and master data management is where this is taking shape.

Andrew White is a research vice president at the analyst firm Gartner. His areas of expertise are ERP and supply chain management.

andrew.white at gartner.com

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